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To reduce the risk and the required bureaucracy for accessing public funding, the EC has developed a mechanism to grant money to consortia to be used to fund to startups, SMEs and Midcaps to start, evolve or transition their offer to increase their competitivity in the market, in turn providing benefit for the entire European economy. This paper presents the experience in managing what is actually 3rd party (or cascading) funding in two different European Innovation Action Projects in the manufacturing domain. The paper discusses the design and implementation phases and provides the detail on how to manage the processes to ensure fairness, efficiency, and effectiveness that is lacking from the EC’s formal documentation.

In recent years, the growing use of Intelligent Personal Agents in different human activities and in various domains led the corresponding research to focus on the design and development of agents that are not limited to interaction with humans and execution of simple tasks. The latest research efforts have introduced Intelligent Personal Agents that utilize Natural Language Understanding (NLU) modules and Machine Learning (ML) techniques in order to have complex dialogues with humans, execute complex plans of actions and effectively control smart devices. To this aim, this article introduces the second generation of the CERTH Intelligent Personal Agent (CIPA) which is based on the RASA framework and utilizes two machine learning models for NLU and dialogue flow classification. CIPA-Generation B provides a dialogue-story generator that is based on the idea of adjacency pairs and multiple intents, that are classifying complex sentences consisting of two users’ intents into two automatic operations. More importantly, the agent can form a plan of actions for implicit Demand-Response and execute it, based on the user’s request and by utilizing AI Planning methods. The introduced CIPA-Generation B has been deployed and tested in a real-world scenario at Centre’s of Research & Technology Hellas (CERTH) nZEB SmartHome in two different domains, energy and health, for multiple intent recognition and dialogue handling. Furthermore, in the energy domain, a scenario that demonstrates how the agent solves an implicit Demand-Response problem has been applied and evaluated. An experimental study with 36 participants further illustrates the usefulness and acceptance of the developed conversational agent-based system.

The path that the European Commission foresees to leverage data in the best possible way for the sake of European citizens and the digital single market clearly addresses the need for a European Data Space. This data space must follow the rules, derived from European values. The European Data Strategy rests on four pillars: (1) Governance framework for access and use; (2) Investments in Europe’s data capabilities and infrastructures; (3) Competences and skills of individuals and SMEs; (4) Common European Data Spaces in nine strategic areas such as industrial manufacturing, mobility, health, and energy. The project BOOST 4.0 developed a prototype for the industrial manufacturing sector, called European Industrial Data Space (EIDS), an endeavour of 53 companies. The publication will show the developed architectural pattern as well as the developed components and introduce the required infrastructure that was developed for the EIDS. Additionally, the population of such a data space with Big Data enabled services and platforms is described and will be enriched with the perspective of the pilots that have been build based on EIDS.

AI is one of the biggest megatrends towards the 4th industrial revolution. Although these technologies promise business sustainability as well as product and process quality, it seems that the ever-changing market demands, the complexity of technologies and fair concerns about privacy, impede broad application and reuse of Artificial Intelligence (AI) models across the industry. To break the entry barriers for these technologies and unleash its full potential, the knowlEdge project will develop a new generation of AI methods, systems, and data management infrastructure. Subsequently, as part of the knowlEdge project we propose several major innovations in the areas of data management, data analytics and knowledge management including (i) a set of AI services that allows the usage of edge deployments as computational and live data infrastructure as well as a continuous learning execution pipeline on the edge, (ii) a digital twin of the shop-floor able to test AI models, (iii) a data management framework deployed along the edge-to-cloud continuum ensuring data quality, privacy and confidentiality, (iv) Human-AI Collaboration and Domain Knowledge Fusion tools for domain experts to inject their experience into the system, (v) a set of standardisation mechanisms for the exchange of trained AI models from one context to another, and (vi) a knowledge marketplace platform to distribute and interchange trained AI models. In this paper, we present a short overview of the EU Project knowlEdge –Towards Artificial Intelligence powered manufacturing services, processes, and products in an edge-to-cloud-knowledge continuum for humans [in-the-loop], which is funded by the Horizon 2020 (H2020) Framework Programme of the European Commission under Grant Agreement 957331. Our overview includes a description of the project’s main concept and methodology as well as the envisioned innovations. The research leading to these results has received funding from the Horizon 2020 Programme of the European Commission under Grant Agreement No. 957331 for EU Project knowlEdge –Towards Artificial Intelligence powered manufacturing services, processes, and products in an edge-to-cloud-knowledge continuum for humans [in-the-loop]. Peer Reviewed Treball signat per 21 autors/autores: Sergio Alvarez-Napagao, Barcelona Supercomputing Center, Spain; Boki Ashmore, ICE, United Kingdom; Marta Barroso, Barcelona Supercomputing Center, Spain; Cristian Barrué, Barcelona Supercomputing Center, Spain; Christian Beecks, University of Münster, Germany; Fabian Berns, University of Münster, Germany; Ilaria Bosi, LINKS Foundation, Italy; Sisay Adugna Chala, Fraunhofer FIT, Germany; Nicola Ciulli, Nextworks, Italy; Marta Garcia-Gasulla, Barcelona Supercomputing Center, Spain; Alexander Grass, Fraunhofer FIT, Germany; Dimosthenis Ioannidis, CERTH/ITI, Greece; Natalia Jakubiak, Universitat Politècnica de Catalunya, Spain; Karl Köpke, Kautex Textron, Germany; Ville Lämsä, VTT Technical Research Centre, Finland; Pedro Megias, Barcelona Supercomputing Center, Spain; Alexandros Nizamis, CERTH/ITI, Greece; Claudio Pastrone, LINKS Foundation, Italy; Rosaria Rossini, LINKS Foundation, Italy; Miquel Sànchez-Marrè, Universitat Politècnica de Catalunya, Spain; Luca Ziliotti, Parmalat, Italy

In recent years, sustainable supply chain management practices have been adopted by companies that desire to reduce the negative environmental and social impacts within their supply chains. Within this perspective, a circular approach has been developed in the supply chain literature. Circular economy models and solutions assisted by industry 4.0 technologies have been developed to transform products in the end of their life cycle into new products with different use. In this paper an industry 4.0 waste-to-energy solution is developed and applied in a pilot case study comprised by a real-world supply chain to evaluate the sustainability performance of circular supply chain management (CSCM). The findings show that redesigning supply chains for circular economy with the use of Industry 4.0 technologies, can enable circular supply chain management. Clear benefits are provided linking the proposed solution to the six circular economy dimensions of the ReSOLVE model i.e. regenerate, share, optimise, loop, virtualise, and exchange. Improved availability of personnel (5% and 15%) and fleet resources (15%) are identified as some of the key quantitative benefits, while supply chain traceability through the full visibility and automation offered by the proposed solution, are some of the key non-quantifiable outcomes. The present work seeks to contribute to the existing literature by providing empirical evidence of how industry 4.0 and circular economy are applied in practice. Implications for managers and policy makers, along with the study limitations and further research paths are also presented.

Nowadays, Intelligent Personal Agents include Natural Language Understanding (NLU) modules, that utilize Machine Learning (ML), which can be included in different kind of applications in order to enable the translation of users’ input into different kinds of actions, as well as ML modules that handle dialogue. This translation is attained by the matching of a user’s sentence with an intent contained in an Agent. This paper introduces the first generation of the CERTH Intelligent Personal Agent (CIPA) which is based on the RASA (https://rasa.com/) framework and utilizes two machine learning models for NLU and dialogue flow classification. Besides the architecture of CIPA—Generation A, a novel dialogue-story generator that is based on the idea of adjacency pairs is introduced. By utilizing on this novel-generator, the agent is able to create all the possible dialog trees in order to handle conversations without training on existing data in contrast with the majority of the current alternative solutions. CIPA supports multiple intents and it is capable of classifying complex sentences consisting of two user’s intents into two automatic operations from the part of the agent. The introduced CIPA—Generation A has been deployed and tested in a real-world scenario at Centre’s of Research & Technology Hellas (CERTH) nZEB Smart Home (https://smarthome.iti.gr/) in two different domains, energy and health domain.

This work presents a cognitive analytics platform for anomaly detection which is capable to handle, analyze and exploit resourcefully machine data from a shop-floor of factory, so as to support the emerging and growing needs of manufacturing industry. The introduced system contributes to industrial digitization and creation of smart factories by providing a generic platform which is a complete solution supporting standards-based factory connectivity, data management, various AI models training and comparisons, live predictions and real-time visualizations. The proposed system is built on a micro-service architecture, in order to be extendable and adaptive over time, and contains three core modules, the Data Acquisition, the Knowledge Management and the Predictive maintenance, which contribute to machine failure prediction and activate predictive maintenance procedures, to efficient production schemes and decision making, to monitor anomalies and handle unforeseen conditions, to predict future behaviours on time series etc. The proposed platform utilizes continuous re-training mechanisms enabling a self learning approach for the delivery of AI solutions, usable also for various production data, guaranteeing the quality of results without continuous monitoring and human-resources allocation for AI models’ retraining. This cognitive platform is supported by machine learning techniques and deep learning architectures in order to achieve the desired performance in the management of factory processes and needs. All the information generated by the proposed platform is provided to the end user through a user interface that utilizes advanced visualization techniques.

Today, the use of IoT devices, the inter-connectivity of machines and systems, and the development of novel analytic algorithms which are main characteristics of Industry 4.0 are also transferred to other domains connected to the industrial one. The advances on industrial domain have impact on waste management one as these domains are strictly connected. The connection with sensors on industrial partners premises, the big data availability and the significant advances on data analytics, enable the waste management companies to smarten their domain and automate many of their solutions and processes. In this work, we present a data analytics platform for the optimization of waste management procedures. By using this platform, a waste management company is able to monitor and analyze sensors data from industrial partners bins and optimize its planning based on different analytic tools and historical data. Besides the platform description, special focus is given on data analytics algorithms and methods.

The EU Factory of the Future (FoF) project COMPOSITION (Ecosystem for COllaborative Manufacturing PrOceSses – Intra- and Interfactory Integration and AutomaTION) aims to develop an Integrated Information Management System (IIMS) in order to combine physical world, simulation, planning and forecasting data for enhancing re-configurability, scalability and optimisation of resources and processes inside a factory. In addition, the second aim of this project is to extend locally integrated information management systems into an ecosystem that supports the interchange of data and services between factories and their suppliers, i.e., by incorporating and inter-linking both supply and value chains. In this paper, we present an overview of the COMPOSITION project with a particular focus on the incorporation of data analytics tools with the Manufacturing Decision Support System (MDSS) so as to enhance its functionalities. Furthermore, the connection of data analytics tools with a smart Building Management System (BMS) and Digital Factory Modelling (DFM) techniques for data propagation are presented as well. We show how state-of-the-art techniques and methodologies from the research fields of big data analytics, deep learning and simulation/forecasting are incorporated into the MDSS, and how the system is designed in order to accommodate the data-driven needs of a manufacturing environment.

The fourth industrial revolution and the digitisation of supply chains have led companies to realize that the adoption of Industry 4.0/Internet of Things (IoT) solutions creates opportunities for more sustainable management. The sustainable management of scrap metal is a challenging task for all the organisations that participate in the supply chain and especially for scrap metal producers and waste management companies. Although metals are considered to be infinitely recycled, scrap metal management is often inefficient due to several factors including collection processes, communication and marketplace limitations, which have significant environmental and economic impacts. The purpose of this paper is to provide evidence of the impact of an IoT solution on the sustainable supply chain management (SSCM) performance. A case study from a scrap metal producer that operates in the lift industry and a waste management company is presented, in order to illustrate how the deployment of a state-of-the-art industry 4.0 solution has the potential to improve sustainability both in the firm level and in the supply chain level. Direct benefits of the introduced solution are the automation of monitoring and negotiation procedures for the produced scrap metal. Indirectly, the proposed solution is beneficial in terms of CO2 emissions’ reduction, resources availability and response time optimization. The results validate the framework for assessing SSCM for Industry 4.0 developed by Manavalan and Jayakrishna (2019) and demonstrate that Ιndustry 4.0 solutions have the potential to improve, among others, the economic, environmental and social sustainability in supply chain management. The present study contributes to the literature by bridging the gap between theoretical developments and real-world cases in the fields of industry 4.0 and SSCM. Managerial implications, limitations and future research opportunities are also provided.

Collaborative manufacturing ecosystems provide a high volume of data, collected from factories and commonly related to machine data, sensor measurements and production processes. Wide variations are noted in these data alongside with the data related to the supply-chain and transactions over the aforementioned ecosystems. Semantics and ontologies are commonly used in order to bridge variances in datasets. Furthermore, deep learning techniques perform different kind of analyses over such high volumes of data. This paper introduces a collaboration scheme between a Semantic Framework and a Deep Learning Toolkit. More precisely, this work describes how the ecosystem’s data were modeled and stored using ontologies, became available and analyzed by the continuous learning algorithms of the Deep Learning Toolkit and finally how they are sent back to the Semantic Framework, enhancing a semantic matchmaker’s efficiency in order to support the automated decision making inside the collaborative ecosystem.

Manufacturing eco-systems aim to connect data and services between factories, suppliers and customers. Most of them are built as agent-based eco-systems which act as web-based operating systems for business connections between enterprises. The connection of supply and demand entities participating in an eco-system by exploiting knowledge and data from the business entities has become imperative for them, in order to adapt to the dynamically changing market requirements. This paper introduces a web-based semantic ontological framework designed for collaborative agent-based manufacturing eco-systems. The proposed framework and its core components enable the information modeling of the manufacturing services and the supply chain concepts. A Collaborative Manufacturing Services Ontology able to describe both manufacturing domain and e-commerce domain is offered alongside with an Application Programming Interface for the effortless manipulation of the ontological resources. Furthermore, a Rule-based Matchmaking engine able to match requesters with possible suppliers, and to evaluate offers from suppliers based on different requesters’ criteria and preferences is provided.

The exploitation of big volumes of data in Industry 4.0 and the increasing development of cognitive systems strongly facilitate the realm of predictive maintenance for real-time decisions and early fault detection in manufacturing and production. Cognitive factories of Industry 4.0 aim to be flexible, adaptive, and reliable, in order to derive an efficient production scheme, handle unforeseen conditions, predict failures, and aid the decision makers. The nature of the data streams available in industrial sites and the lack of annotated reference data or expert labels create the challenge to design augmented and combined data analytics solutions. This paper introduces a cognitive analytics, self- and autonomous-learned system bearing predictive maintenance solutions for Industry 4.0. A complete methodology for real-time anomaly detection on industrial data and its application on injection molding machines are presented in this study. Ensemble prediction models are implemented on the top of supervised and unsupervised learners and build a compound prediction model of historical data utilizing different algorithms’ outputs to a common consensus. The generated models are deployed on a real-time monitoring system, detecting faults in real-time incoming data streams. The key strength of the proposed system is the cognitive mechanism which encompasses a real-time self-retraining functionality based on a novel double-oriented evaluation objective, a data-driven and a model-based one. The presented application aims to support maintenance activities from injection molding machines’ operators and demonstrate the advances that can be offered by exploiting artificial intelligence capabilities in Industry 4.0.

Blockchain technology back in 2009 was mainly used for finance use cases due to the cryptocurrency support of the Bitcoin and the Ethereum networks. Nowadays with the emergence of business oriented distributed ledger frameworks we can find blockchain (BC) in almost every aspect of our everyday real-life transactions. Starting with supply chain, BC has been used to support and secure education, e-government, real estate, insurance, healthcare and other business cases. Regarding blockchain in supply chain, examples as the Walmart and IBM partnership shows that smart contracts (SC) and cryptocurrencies can disrupt the way suppliers, shippers, retailers and customers trust each other and interact. This paper describes how we applied BC technology in two real-life supply chain scenarios. The first one is for logging and tracing products, showing how we can use SCs to identify the ingredients of food products and how to uniquely identify the food product throughout its shipment from the factory to the customer who purchase it. Important aspects of this route is the transparency of the process as well as the verification of transport. The second one deals with how authentication works for users holding BC identities. The identity of a user is important in order to secure the network, allowing only permissioned parties to access it and perform actions on the data. As long as the network is private and immutable, authenticating and authorizing users is crucial for enforcing the end-to-end security to increase users' trust and protect the confidentiality of data. While exploring these two scenarios we define the actors, the transactions these actors can perform on the systems and we propose the SCs that have to be implemented for these use cases to take advantage of BC's unique characteristics, as immutability and non-repudiation. In addition, we provide sample template SCs for both scenarios.